H.264/AVC in wireless environments
By Thomas Stockhammer, Miska M. Hannuksela, Thomas Wiegand. IEEE Trans. Circuits Syst. Video Technol., 2003
611 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/6dd66f0bff9ccab2bd4f15bfc01f198572f46548
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- A. Bovik
- Thomas Wiegand
- Anush K. Moorthy
- Rajiv Soundararajan
- Gary J. Sullivan
- Stephan Wenger
- Zhou Wang
- Damon M. Chandler
In Signal Processing
- Thomas Wiegand
- Gary J. Sullivan
- Stephan Wenger
- Thomas Stockhammer
- Detlev Marpe
- Ajay Luthra
- Gisle Bjøntegaard
- A. Bovik
Citations in the corpus
- Video coding with H.264/AVC: tools, performance, and complexity (2004, methodology): “For most successful applications, a selection of reference frames and intra updates can be integrated in a rate-distortion optimized encoder control as discussed in Section 4 taking into account not only video statistics, but also all available channel information [7].”
- Rate-constrained coder control and comparison of video coding standards (2003, neutral): “error-prone transmission, please refer to [19].”
- Study of Subjective and Objective Quality Assessment of Video (2010, methodology): “264 video over wireless networks can be found in [15].”
- Overview of the H.264/AVC video coding standard (2003, methodology): “VCL As in all prior ITU-T and ISO/IEC JTC1 video standards since H.261 [3], the VCL design follows the so-called blockbased hybrid video coding approach (as depicted in Fig.”
- H.264/AVC over IP (2003, methodology): “(Please see [2] in this Special Issue for an in-depth discussion.)”
- Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies (2012, methodology): “For each video, a random error-trace from the set of 100 traces was picked and applied, where a video packet was considered to be lost if one of the bits of the packet was erroneous [27].”
- Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems (2009, methodology): “For example, in HSDPA systems ARQ, adaptive modulation schemes, and multi-user scheduling considering the channel states are combined to significantly enhance the throughput [29].”